BMW Case Study - Gabe Campbell

Lumin Digital • 2021 - Present Google • 2020 - 2021 BMW • 2018 - 2020

Smart Charging for a Smarter Grid

How I pioneered BMW's first dynamic charging system, using machine learning to balance power grid load while creating a delightful experience for electric vehicle drivers.

First Dynamic Charging

$4.2M Revenue Generated

87% Grid Load Reduced

The Challenge

Electric vehicles were creating a new problem: everyone charged at the same time.

As BMW launched their electric vehicle line, we discovered an unexpected issue. Most drivers plugged in their cars between 6-8 PM, creating massive spikes in power demand. This threatened to overwhelm power grids and increase electricity costs for everyone.

We needed to create an app that would intelligently distribute charging throughout the night while ensuring every driver had a full battery by morning. The challenge? Making grid optimization feel magical, not restrictive.

Peak Load Distribution

15%

12am

10%

3am

12%

6am

20%

9am

18%

12pm

22%

3pm

87%

6pm

82%

7pm

75%

8pm

25%

9pm

Peak usage hours creating grid strain Normal usage periods

Research & Discovery

Understanding drivers' needs, anxieties, and behaviors around EV charging.

User Personas

🔧

Michael Chen

Software Engineer

Age 42

Location Palo Alto, CA

Daily Commute 65 miles

Charging Setup Level 2 Home

Primary Needs

Granular control over charging parameters

API access for home automation

Detailed analytics and usage data

"I built my own smart home system. I need my car's charging to integrate seamlessly with my solar panels and time-of-use rates."

🌱

Sarah Martinez

Sustainability Director

Age 38

Location Berkeley, CA

Daily Commute 28 miles

Charging Setup Workplace + Home

Primary Needs

Carbon footprint tracking

Green energy source preferences

Community impact visibility

"I track every aspect of my environmental impact. Show me exactly how my charging choices affect the grid's renewable energy usage."

👔

David Thompson

Sales Executive

Age 48

Location San Jose, CA

Daily Commute Variable (40-120)

Charging Setup Home only

Primary Needs

Guaranteed readiness for unexpected trips

Cost optimization without complexity

Set-and-forget reliability

"I have client emergencies at random times. I need my car ready to go, but I also don't want to think about it or pay more than necessary."

Insight 1: Range Anxiety is Real

73% of drivers worried their car wouldn't be charged when needed, even with smart scheduling.

Insight 2: Transparency Builds Trust

Users wanted to see exactly when and why their car would charge at specific times.

Insight 3: Money Talks

Showing actual dollar savings was 3x more effective than environmental benefits alone.

Design Process

From rough sketches to polished interfaces, iterating based on user feedback.

Early Design Explorations

Initial wireframe explorations focusing on different user needs and interaction patterns

The Solution

An intelligent system that makes doing the right thing feel effortless, and feels so minimalist and simple that users can't help but use it.

🧠

Predictive Algorithm

Co-developed a machine learning algorithm that predicted optimal charging times based on grid load and driving patterns.

⚡

Transparent Control

Designed an interface that gave drivers full visibility and control over their charging schedule.

🎮

Incentive Design

Created a rewards system that made grid-friendly charging feel like winning.

Dashboard - At-a-glance charging status

Smart Schedule - Optimized charging

Insights - Impact visualization

Settings - User control

The Impact

Real results that transformed how people think about charging.

⚡ 87% Peak Load Reduction

💰 $840 Avg Annual Savings

😊 94% User Satisfaction

🌍 2.3M kg CO2 Reduced

Key Learnings

What building the future of mobility taught me.

Make the right choice the easy choice.

By optimizing for user convenience first, we achieved grid optimization as a natural outcome.

Transparency builds trust.

Showing users exactly how the system worked made them more likely to participate.

Small incentives drive big changes.

Gamification transformed charging from a chore into something drivers looked forward to.

Design for the ecosystem.

By considering all stakeholders, we created a solution that worked for everyone.